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Can convolution neural network (CNN) triumph in ear recognition of uniform illumination invariant?

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dc.citedby8
dc.contributor.authorJamil N.en_US
dc.contributor.authorAlmisreb A.A.en_US
dc.contributor.authorAriffin S.M.Z.S.Z.en_US
dc.contributor.authorMd Din N.en_US
dc.contributor.authorHamzah R.en_US
dc.contributor.authorid6603538109en_US
dc.contributor.authorid50460937600en_US
dc.contributor.authorid56494612000en_US
dc.contributor.authorid9335429400en_US
dc.contributor.authorid55516675400en_US
dc.date.accessioned2023-05-29T06:51:25Z
dc.date.available2023-05-29T06:51:25Z
dc.date.issued2018
dc.description.abstractCurrent deep convolution neural network (CNN) has shown to achieve superior performance on a number of computer vision tasks such as image recognition, classification and object detection. The deep network was also tested for view-invariance, robustness and illumination invariance. However, the CNN architecture has thus far only been tested on non-uniform illumination invariant. Can CNN perform equally well for very underexposed or overexposed images or known as uniform illumination invariant? This is the gap that we are addressing in this paper. In our work, we collected ear images under different uniform illumination conditions with lumens or lux values ranging from 2 lux to 10,700 lux. A total of 1,100 left and right ear images from 55 subjects are captured under natural illumination conditions. As CNN requires considerably large amount of data, the ear images are further rotated at every 5o angles to generate 25,300 images. For each subject, 50 images are used as validation/testing dataset, while the remaining images are used as training datasets. Our proposed CNN model is then trained from scratch and validation and testing results showed recognition accuracy of 97%. The results showed that 100% accuracy is achieved for images with lumens ranging above 30 but having problem with lumens less than 10 lux. � 2018 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijeecs.v11.i2.pp558-566
dc.identifier.epage566
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85048180302
dc.identifier.spage558
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85048180302&doi=10.11591%2fijeecs.v11.i2.pp558-566&partnerID=40&md5=bbfa7652cfe10ed7da8d9c31f7f0b19d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23741
dc.identifier.volume11
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Hybrid Gold, Green
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Computer Science
dc.titleCan convolution neural network (CNN) triumph in ear recognition of uniform illumination invariant?en_US
dc.typeArticleen_US
dspace.entity.typePublication
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